Automatic Mapping of Natural Terrestrial Habitats in the French Southern Territories

Implemented on Possession Island (Crozet Archipelago) as well as on St. Paul and Amsterdam Islands, and adaptable to other territories, Cartovege has developed:

• a rigorous method for defining and classifying habitats specific to the Austral Lands,
• a data processing pipeline for field data and multi-source imagery, capable of automatically generating a very high-resolution habitat map - including for unvisited areas - based on satellite imagery.

This work has led to the creation of a habitat guide for Crozet, freely available from Quae Publishing (here), and soon for Amsterdam Island.

🟢 Open-source processing pipeline here

Satellite mapping of green spaces for green and healthy citiesImplemented in five European cities, the GreenSpace service combines satellite and scientific data to create a comprehensive inventory of public and urban tree populations, realistically quantify the removal of key air pollutants and greenhouse gases by these trees, and provide recommendations on the best species to plant. It thus enables cities with at least 20,000 residents to develop ambitious greening plans in response to environmental guidelines.

🔵 Service marketed by ACRI-ST

Supporting solutions based on urban vegetation

Supported by Cerema, the GUS project has developed a method for fine mapping urban vegetation in order to better assess its many ecosystem services. The results obtained in Greater Nancy are available online on the Landia platform (ex Green City) operated by TerraNIS. This platform offers summary indicators at different grids (hexagonal, urban morphological island, IRIS, municipalities, etc.), data explorers and summary dashboards.

🟢 Access to the Nancy demonstrator is open.

🟢 Based almost entirely on THR satellite images, the method can be replicated for any town => the algorithmic codes are available on Cerema's Github.

Identify and visualize agricultural wasteland

The Izifriche^® solution is a comprehensive support service for the identification and visualization of agricultural wasteland, aimed at local players wishing to act for the resilience of their territory.

Developed as part of the SCO FrichesAgricoles project, the method for automated identification of wasteland is based on the use of geographical and satellite data analyzed by the WaSaBI ©CNES software's Artificial Intelligence, at the scale of the entire Occitanie region (France). The parcel data from the regional inventory of wasteland is used in a web interface equipped with numerous geographic databases, providing a global vision of the issues at stake at different territorial scales (commune, inter-commune, département).

🔵 Service marketed by Safer Occitanie

Coastline monitored by satellite

Littosat distributes seasonal image mosaics at high and low tide, systematically and at high spatial resolution (10 m). From these data, it generates high value-added products on foreshore vegetation, and will soon offer products on submerged vegetation and foreshore morphodynamics, feeding seasonal change detection functions on a regional scale.

4 interfaces are online: Littosat Brittany, Littosat Normandy, Littosat Atlantic, and Littosat Gulf of Lion

🟢 Free access

Dynamic Monitoring of Peatlands

Implemented as part of the AI2Peat project, the PeatSense portal enables the collection and sharing of reliable data on the condition of peatlands. By combining satellite imagery and artificial intelligence, the tool generates various maps of peatland condition at any spatial or temporal scale. Developed in Ireland, it is currently being implemented in the United Kingdom and Scandinavia.

🟢 Free access